This paper aims to investigate how automatic classification can assist employees and records managers with the appraisal of e-mails as records of value for the organization.
The study performed a qualitative analysis of the appraisal behaviours of eight records management experts to train a series of support vector machine classifiers to replicate the decision process for identifying e-mails of business value. Automatic classification experiments were performed on a corpus of 846 e-mails from two of these experts’ mailboxes.
Despite the highly contextual nature of record value, these experiments show that classifiers have a high degree of accuracy. Unlike existing manual practices in corporate e-mail archiving, machine classification models are not highly dependent on features such as the identity of the sender and receiver or on threading, forwarding or importance flags. Rather, the dominant discriminating features are textual features from the e-mail body and subject field.
The need to automatically classify corporate e-mails is growing in importance, as e-mail remains one of the prevalent recordkeeping challenges.
Automated methods for identifying e-mail records promise to be of significant benefit to organizations that need to appraise e-mail for long-term preservation and access on demand.
The research adopts an innovative approach to assist employees and records managers with the appraisal of digital records. By doing so, the research fosters new insights on the adoption of technological strategies to automate recordkeeping tasks, an important research gap.
Our experiment show that a SVM classifier can be trained to replicate an expert's decision process for identifying e-mails of business value with a reasonably high degree of accuracy. In principle, such a classifier could be integrated into a corporate Electronic Document and Records Management System (EDRMS) to improve the quality of e-mail records appraisal.
The authors would like to thank the participants for their generous contribution to this research and to the anonymous reviewers for their helpful suggestions.
Funding: This research was supported by a research grant from the Faculty of Arts, University of Ottawa.
Vellino, A. and Alberts, I. (2016), "Assisting the appraisal of e-mail records with automatic classification", Records Management Journal, Vol. 26 No. 3, pp. 293-313. https://doi.org/10.1108/RMJ-02-2016-0006Download as .RIS
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